The genetic variation underlying atorvastatin (ATV) pharmacokinetics was evaluated in a Mexican population. Aims of this study were: 1) to reveal the frequency of 87 polymorphisms in 36 genes related to drug metabolism in healthy Mexican volunteers, 2) to evaluate the impact of these polymorphisms on ATV pharmacokinetics, 3) to classify the ATV metabolic phenotypes of healthy volunteers, and 4) to investigate a possible association between genotypes and metabolizer phenotypes.
León-Cachón et al BMC Cancer (2016) 16:74 DOI 10.1186/s12885-016-2062-2 RESEARCH ARTICLE Open Access A pharmacogenetic pilot study reveals MTHFR, DRD3, and MDR1 polymorphisms as biomarker candidates for slow atorvastatin metabolizers Rafael B R Ln-Cachón1,2, Jorge A Ascacio-Martínez2, María E Gamino-Pa3, Ricardo M Cerda-Flores4, Irene Meester1, Hugo L Gallardo-Blanco5, Magdalena Gómez-Silva3, Everardo Piñeyro-Garza3 and Hugo A Barrera-Saldaña2,6* Abstract Background: The genetic variation underlying atorvastatin (ATV) pharmacokinetics was evaluated in a Mexican population Aims of this study were: 1) to reveal the frequency of 87 polymorphisms in 36 genes related to drug metabolism in healthy Mexican volunteers, 2) to evaluate the impact of these polymorphisms on ATV pharmacokinetics, 3) to classify the ATV metabolic phenotypes of healthy volunteers, and 4) to investigate a possible association between genotypes and metabolizer phenotypes Methods: A pharmacokinetic study of ATV (single 80-mg dose) was conducted in 60 healthy male volunteers ATV plasma concentrations were measured by high-performance liquid chromatography mass spectrometry Pharmacokinetic parameters were calculated by the non-compartmental method The polymorphisms were determined with the PHARMAchip® microarray and the TaqMan® probes genotyping assay Results: Three metabolic phenotypes were found in our population: slow, normal, and rapid Six gene polymorphisms were found to have a significant effect on ATV pharmacokinetics: MTHFR (rs1801133), DRD3 (rs6280), GSTM3 (rs1799735), TNFα (rs1800629), MDR1 (rs1045642), and SLCO1B1 (rs4149056) The combination of MTHFR, DRD3 and MDR1 polymorphisms associated with a slow ATV metabolizer phenotype Conclusion: Further studies using a genetic preselection method and a larger population are needed to confirm these polymorphisms as predictive biomarkers for ATV slow metabolizers Trial registration: Australian New Zealand Clinical Trials Registry: ACTRN12614000851662, date registered: August 8, 2014 Keywords: Atorvastatin, Genotype phenotype association, Predictive genetic testing Background The drug atorvastatin (ATV) is widely prescribed to treat hypercholesterolemia, which is a predisposing factor for developing atherosclerosis ATV, like all statins, acts by inhibiting 3-hydroxy-3-methylglutaryl-coenzyme A reductase, an essential enzyme in cholesterol * Correspondence: habarrera@gmail.com Departamento de Bioquímica y Medicina Molecular, Facultad de Medicina, Universidad Autónoma de Nuevo León, Monterrey, NL, México Vitagénesis S.A., Monterrey, NL, México Full list of author information is available at the end of the article biosynthesis ATV reduces the risk of atherosclerosis by lowering the levels of low-density lipoprotein-bound cholesterol [1] ATV is administered orally, as a calcium salt (acid form), at a dose that ranges between 10 and 80 mg/day Once ATV is ingested, several enzymes participate in its metabolism The enzymes encoded by CYP3A4 and CYP3A5 are the most important ones [2] These enzymes transform ATV first to its lactone form and subsequently into pharmacologically active metabolites (2-hydroxy-ATV and 4-hydroxy-ATV) [3, 4] Next, ATV and its secondary metabolites are glucuronidated © 2016 León-Cachón et al Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated León-Cachón et al BMC Cancer (2016) 16:74 Page of 13 by uridine diphosphoglucuronosyltransferases (encoded by UGT1A1 and UGT1A3) [5] Apart from metabolic enzymes, carrier proteins are involved in ATV metabolism, such as: 1) P-glycoprotein, also known as multidrug resistance protein (MDR1), product of ABCB1, 2) the organic anion-transporting polypeptides (OATP1B1 and OATP1B3) encoded by SCLO1B1 and SCLO1B3, respectively [6–8], and 3) the breast cancer resistance protein (BCRP2), product of ABCG2 [9, 10] Drug performance is evaluated on two main areas: pharmacokinetics and pharmacodynamics The former focuses primarily on the evaluation of absorption, distribution, metabolism, and excretion processes (ADME) of a drug; consequently, the variation in genes responsible for this process contributes to interindividual variability [11] On the other hand, pharmacodynamics evaluates biochemical and physiological effects, as well as the mechanisms of action of a drug, i.e it focuses on the drug response [12] The pharmacokinetics of ATV displays high interindividual variability of up to 30 % [13, 14] This pharmacokinetic discrepancy reveals variations in the ADME processes as the drug passes through the human body [12] Factors such as age, gender, ethnicity, and genetic variability are involved in such interindividual differences [15, 16] Genetic factors are responsible for about 15 to 30 % of the interindividual variation in metabolism, and thus in the response of each patient to certain classes of drugs However, for some drugs this percentage can increase to up to 95 % [17] These genetic factors are mainly due to variations in short tandem repeats, copy number variations, insertions and deletions, and single-nucleotide polymorphisms (SNPs) The latter is the most common source of variation [18–20] The presence of these polymorphisms in genes involved in the metabolism of ATV may explain its pharmacokinetic variability [17], since the frequency and consequences also vary between different populations [21] The aims of this study were: 1) to reveal the frequency of 87 polymorphisms in 36 genes related to drug metabolism in healthy volunteers, 2) to evaluate the impact of these polymorphisms on ATV pharmacokinetics, 3) to classify the metabolic phenotypes for ATV, and 4) to investigate a possible association between genotypes and phenotypes SSA1-1998) [22] Furthermore, the protocol was approved by the Research and Ethics Committee of the pharmacokinetic study center, Ipharma S.A (Monterrey, Mexico) The clinical study has been registered at the Australian New Zealand Clinical Trials Registry (registration number: ACTRN12614000851662) Methods Pharmacogenetic tests Design Leukocytes were obtained from the buffy coat, and genomic DNA was extracted by the alkaline lysis method [25] Seven multiplex polymerase chain reactions (PCRs) amplified the desired gene regions, following a validated protocol [26] Screening for gene polymorphisms was performed using the PHARMAchip® microarray (Progenika, Derio, ES) This pharmacogenetic genotyping device detects, with a 99.9 % specificity and sensitivity, 85 A randomized pilot study was carried out in 60 healthy Mexican volunteers to determine ATV pharmacokinetic parameters A single dose of 80 mg ATV was administered The clinical study complied with Good Clinical Practice standards, the guidelines of the Declarations of Helsinki and Tokyo, and the Mexican regulations on Bioavailability and Bioequivalence Studies (NOM-177- Study population Sixty four healthy male candidates from northeastern Mexico were recruited and a written informed consent was obtained Inclusion criteria were: non-smoker, 18to-45-year old, weight ≥ 50 kg, body mass index (BMI) of 20–26 kg/m2, availability for completing the study, being healthy Since ATV is classified as a pregnancy category X drug, only males were considered for the study Candidates were excluded for: any abnormal lab result, significant personal or family medical history of angioedema o allergies, the existence of concurrent disease, use of prescription or over-the-counter medication or alcohol before enrollment, history of smoking, alcohol or drug abuse, and incompliance or non-willingness to complete the study Four candidates were excluded because of the consumption of alcohol and/or substances or an altered blood pressure The health status of the volunteers was confirmed by a medical history, a physical examination, an electrocardiogram (ECG), laboratory tests (blood count, blood chemistry, liver function tests, and urinalysis), and seronegativity for human immunodeficiency virus and hepatitis B and C viruses Drug administration and sampling After an overnight fast at the study center (Ipharma, S A.), each subject was given a single dose of 80 mg of ATV-coated tablets (Pfizer Pharmaceuticals LLC, Caguas Site, Caguas, PR) The volunteers were under direct medical supervision at the study site Venous blood (4 mL) was collected in K2EDTA-coated VacutainersTM (BD Diagnostics, Franklin Lakes, NJ, US), before ATV administration (time 0), and at the following time points after drug administration: 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 3.5, 4, 5, 6, 8, 12, 24, 36 and 48 h Plasma was separated by centrifugation (15 at 1600 g at °C) and stored in cryovials at −80 °C until analysis, using a method validated by Ipharma S A [3, 23, 24] León-Cachón et al BMC Cancer (2016) 16:74 gene polymorphisms in 34 genes involved in drug metabolism and response, including those encoding cytochrome P450 enzymes, phase II metabolism enzymes, receptors, and transporters Amplified products were fractionated with DNAse according to a validated protocol [26], followed by fluorescent labeling and hybridization of the microarray, in an automated TECAN HS4800PRO platform (Ventana Medical Systems Inc., Tucson, AZ, US) The hybridization pattern was revealed using the Innoscan 710 scanner (Innopsys S.A., Carbonne, FR) Polymorphic variants were determined using PHARMAchip software V.3.2.9 [26] Two additional polymorphisms not included in the PHARMAchip, rs2231142 (C 15854163_70) and rs4149056 (C 30633906_10), in ABCG2 and SLCO1B1 respectively, were included in the study and analyzed by RealTime PCR system using validated Genotyping Assays (Applied Biosystems, Foster City, CA, US) according to the manufacturer’s instructions Typed polymorphisms were only includedin subsequent association studies after having passed three quality control tests: the genotype call rate (>0.90 completeness to obtain 99.8 % accuracy), the Hardy-Weinberg equilibrium (HWE) test (P-value > 0.05), and the minor allele frequency (MAF) criterion (>0.01) Determination of ATV calcium in plasma Proteins were eliminated from the plasma samples by adding volumes of acetonitrile to 100-μL samples, vortexing (70 rpm, min.), and precipitating by centrifugation (9600 g, 10 min., 10 °C) Protein-free supernatant (300 μL) was recovered and 5-μL samples were injected into an Agilent 1100 high-performance liquid chromatographer (HPLC; equipped with an autosampler and a binary pump), which was connected to an Agilent 6410 tandem mass spectrometer (MS/MS) with a triple quadrupole detector (Agilent Technologies, Santa Clara, CA, US) to measure ATV calcium levels A C18 pre-column and a SynergiTM Fusion-RP column (4 μm, 80 Å, 50 × mm; Phenomenex, Torrance, CA, US) formed the solid phase, whereas the mobile phase consisted of 0.03 % formic acid/ 70 % acetonitrile in analytical grade water The column temperature was 40 °C, the flow rate 0.4 mL/min, and the auto-sampler temperature 20 °C The detection system used an ESI MS/MS precursor ion (+) 559.3 m/z and a product ion (+) 440.3 m/z Under these conditions, interday linearity was assessed by performing calibration curves from 0.5 to 100 ng/mL (0.5, 2.5, 5, 10, 25, 50 and 100); intraday quality control was evaluated by using eight ATV control samples of 1.7, 7.5, 35, and 75 ng/mL each Pharmacokinetic analysis WinNonlin® professional software V.5.3 (Pharsight Corp., Mountain View, CA, US) was used for pharmacokinetic Page of 13 analysis The maximum plasma concentration (Cmax) and the time to reach Cmax (Tmax) were calculated from the observed concentration-time data in plasma Pharmacokinetic parameters were estimated with the noncompartmental method after oral administration of a single dose of ATV and were as follows: 1) the area under the plasma concentration-time curve from time to the time of the last measurement (AUC0-t), calculated using the logarithmic-linear trapezoidal rule, 2) the area under the curve from time to the time extrapolated to infinity (AUC0-∞), 3) the apparent clearance of the fraction dose absorbed (Cl/F), 4) the elimination rate constant in the terminal phase (Ke), and 5) the half-life in the terminal phase of the drug (T1/2) Statistical analyses For sample size calculation, it was assumed that the coefficient of variation (CV) was 45 % for the Cmax and AUC of ATV Considering a significance level of %, a minimum power of 80 %, an Ω of 0.25, and a confidence interval of 90 %, a sample size of 58 would suffice The metabolizer phenotypes classification was made using a multivariate analysis of the combined pharmacokinetics parameters Cmax and AUC0-t To minimize the effect of scale differences, before calculating the distance matrix, these variables were standardized Next, the individual values of Cmax and AUC0-t were subjected to hierarchical cluster analysis (HCA) using the Ward linkage method and the interindividual Manhattan distances were computed The standardization, HCA, and the hierarchical clustering dendogram were made using Minitab 16 demo software (Minitab Inc., State College, PA, US) [27] We identified the participants of each cluster and calculated the geometric means of all pharmacokinetic parameters of each cluster According to the geometric means of the pharmacokinetic parameters of the clusters they were classified into metabolizer phenotypes Next, one-way ANOVA and the Kruskal-Wallis H test were used to validate the classification model The HWE was determined by comparing the genotype frequencies with the expected values using the maximum likelihood method [28] To detect significant differences between groups, Student’s t-test or the Mann Whitney U test were used for parametric and non-parametric distributions, respectively Differences between more than groups were assessed by one-way ANOVA or the Kruskal-Wallis H test for parametric and nonparametric distributions, respectively Post hoc tests (LSD and Tamhane’s T2) were used for pairwise comparisons To evaluate the contribution of genetic factors to the variability of the pharmacokinetic parameters linear regression analysis was done Possible associations between genotypes or genotype combinations and phenotypes were assesed using contingency tables Χ2 statistics León-Cachón et al BMC Cancer (2016) 16:74 Page of 13 and Fisher’s exact tests The linear regression analysis and association studies were performed under three different models (dominant, recessive, and additive) Odds ratios were estimated with 95 % confidence intervals The model for prediction was confirmed using stepwise multiple linear regression analysis Aforementioned analyses were performed with SPSS for Windows, V.20 (IBM Corp., Armonk, NY, US) All P-values were twotailed The corrected P (Pc) values were adjusted according to Bonferroni’s correction for multiple comparisons and the Benajmini-Hochberg procedure was applied to exclude spurious associations [29] A P-value ≤ 0.05 was considered statistically significant Results Study population Sixty male subjects completed the study Volunteers were of mestizo descent, most of them students (73 %) from the state of Nuevo Leon (83 %) Other demographic characteristics did not display significant variability (Table 1) ATV pharmacokinetics Despite controlling physiological and environmental conditions the pharmacokinetic parameters were highly variable (Table 2) The geometric mean ± SD for the pharmacokinetic parameters obtained were: Cmax = 41.44 ± 23.35 ng/mL, AUC0-t = 141.88 ± 86.78 ng/mL*h, AUC0-∞ = 157.12 ± 87.24 ng/mL*hr, Cl/F = 509.20 ± 265.57 L/h, T1/2 = 9.81 ± 6.58 h, and the Ke = 0.071 ± 0.035 Classification of metabolizer phenotypes Cmax and AUC0-t were used for HCA classification of pharmacokinetic profiles, because Cmax tends to best reveal differences in pharmacokinetic profiles and AUC0-t is considered to be the best parameter to evaluate a drug’s interindividual pharmacokinetic variation [30] The HCA, based on centroid distance, revealed three main clusters Which we identified as slow metabolizers (30.00 %), normal metabolizers (41.66 %), and rapid metabolizers (28.33 %), as shown in Fig The geometric means of the pharmacokinetics parameters were significantly different among the three clusters (P ≤ 0.016), except for T1/2 (Table 2) The mean concentration-time profile and the geometric mean pharmacokinetic parameters of ATV obtained for each metabolizer phenotype are shown in Fig 2a and Table 2, respectively We observed a > 9-fold difference in ATV pharmacokinetic paratmeters between the fastest metabolizer indivual (Cmax = 10.94 ng/mL and AUC0-t = 55.23 ng/mL*h) and the slowest metabolizer individual (Cmax = 101.85 ng/mL and AUC0-t = 454.41 ng/mL*h) The distribution of phenotypes regarding Cmax-AUC0-t values, are presented in Fig 2b Pharmacogenetic tests The allele and genotype frequencies of the gene polymorphisms with a potential impact on drug metabolism are presented in Additional file The HWE applied to most of the gene polymorphisms, with the exception of the gene deletions of GSTM1 and GSTT1, because the heterozygous variants were not detected [31] The polymorphism rs1800896 in the IL10 was not in HWE equilibrium The polymorphisms in CYP2D6, NAT2, TPMT, and TYMS were below the call rate threshold of 0.9 The SNPs in DPYD, rs1799807 in BCHE, and rs28399504 and rs41291556 in CYP2C19 had a MAF < 0.01 The aforementioned polymorphisms were excluded from subsequent analyses; a total of 30 SNPs remained for statistical analysis Association between gene polymorphisms and ATV pharmacokinetics The various pharmacokinetic parameters were affected differentially by the different genetic loci;i.e a certain polymorphism had an effect on Cmax but not on AUC0-t or the other way round, while there were also polymorphisms that affected both and/or other parameters (Table 3) The effect of MTHFR-rs1801133 on Cmax was statistically significant Heterozygous variant (C/T) carriers and homozygous variant (T/T) carriers had lower Cmax values compared to homozygous wild-type (C/C) carriers (P = 0.018 and 0.004, respectively) Carriers of the variant genotype (C/T or T/T) showed significant lower values of Cmax (P = 0.006), AUC0-t (P = 0.050) and AUC0-∞ (P = 0.044) but statistically significant higher values of Cl/F (P = 0.044) as compared to homozygous wild-type subjects (C/C) The genotypes resulting from the DRD3-rs6280 (Ser9Gly) polymorphism had a significant impact on ATV pharmacokinetics First, the homozygous wild-type (C/C) carriers had lower T1/2 values (P = 0.003) and higher Ke values (P = 0.008) as compared to homozygous variant (T/T) carriers Second, when comparing T/T with (C/T) genotypes, there were significant differences for AUC0-t, AUC0-∞, Cl/F, T1/2 and Ke values Table Demographic data of volunteers Mean SD Gender BMI (kg/m2) BS (m2) Age (years) Weight (kg) M 23.43 1.84 24.01 1.64 0.131 4.35 BMI body mass index; BS body surface area; M male SD Standard Deviation Data shown as mean (± SD) Height (m) n 70.58 1.73 60 8.24 0.065 León-Cachón et al BMC Cancer (2016) 16:74 Page of 13 Table Pharmacokinetic parameters according to metabolizer phenotype All subjects Parameters N Phenotype Slow 60 Cmax (ng/mL) Normal 18 Rapid 25 17 41.44 ± 23.35 75.39 ± 15.74* 40.48 ± 6.37* 22.11 ± 8.15* 141.88 ± 86.78 218.14 ± 101.90* 152.69 ± 48.83* 80.78 ± 27.24* AUC0-∞ (ng/mL*h) 157.12 ± 87.24 231.55 ± 100.02* 166.35 ± 55.59* 95.81 ± 28.38* Cl/F (L/h) 509.20 ± 265.57 345.49 ± 119.94* 480.93 ± 152.07* 834.97 ± 249.52* AUC0-t (ng/mL*h) T1/2 (h) Ke 9.81 ± 6.58 9.64 ± 6.18 0.071 ± 0.035 0.072 ± 0.041* 10.76 ± 7.54 0.064 ± 0.030* 8.71 ± 5.43 *0.080 ± 0.035 Data shown as geometric mean (± Standard Deviation) *statistically significant (P ≤ 0.016) (P were 0.027, 0.024, 0.024, 0.027, and 0.041, respectively) Third, the presence of the wild-type allele, combination (C/C + C/T), had a significant influence on all pharmacokinetics parameters (P values: Cmax = 0.050, AUC0-t = 0.026, AUC0-∞ = 0.016, Cl/F = 0.016, T1/2 = 0.004, and Ke = 0.007) GSTM3- rs1799735 had a significant effect on AUC0-∞ and Cl/F when *A/*A and *A/*B were compared (P = 0.041 for both parameters) Regarding TNF-rs1800629, the A allele carriers had significantly higher AUC0-t, AUC0-∞, and T1/2 values (P = 0.035, 0.030, and 0.025, respectively) and lower Cl/F and Ke values (P = 0.030 and 0.025) The homozygous variant (A/A) carrier was not found MDR1 (ABCB1) and OATP1B1 (SLCO1B1) were the only ATV transportrelated genes with an effect on ATV pharmacokinetics The variant allele (T) of MDR1-rs1045642 produced a significant increase of Cmax (P = 0.037) when the combination of the heterozygous and homozygous variant (C/T + T/T) is compared with the homozygous wildtype (C/C) The C allele of the SLCO1B1-rs4149056 polymorphism significantly affected AUC0-t, AUC0-∞, and Cl/F values (P = 0.004 for all three parameters) in homozygous wild-type allele and heterozygous (C/C + C/ T) carriers None of the other 24 polymorphisms tested had a significant impact on ATV pharmacokinetics The influences of polymorphisms on the ATV pharmacokinetics are shown in Table Association between genotypes and metabolizer phenotypes No individual genotype correlated with any metabolizer phenotype after Bonferroni’s correction for multiple testing However, MDR1-rs1045642 behavior was remarkable in this aspect, as no homozygous wild-type (C/C) was a slow metabolizer Of the six polymorphisms with an effect on ATV pharmacokinetics, three polymorphisms associated with the slow metabolizer phenotype considering genetic models The C/T or T/T genotype of MTHFRrs1801133, the T/T genotype of DRD3-rs6280, and the C/T or T/T genotype of MDR1-rs1045642 were significantly associated with slow metabolizer phenotype using Fig Classification of ATV metabolic phenotypes A dendrogram generated with the Manhattan distance and Ward’s linkage method illustrates rapid metabolizers (red), normal metabolizers (green), and slow metabolizers (blue) León-Cachón et al BMC Cancer (2016) 16:74 Page of 13 Fig Distinctive pharmacokinetic profiles of rapid, normal and slow metabolizers a Mean peak plasma ATV concentration-time curves after a single 80-mg dose of ATV of the three metabolizer phenotypes Data shown are mean ± standard error (SE) concentrations b Distributions of metabolizer phenotypes with regard to Cmax-AUC0>-t values For both a xand b: rapid metabolizers in red, normal in green, and slow ones in blue dominant, recessive, and dominant models, respectively This association remained statistically significant after adjusting for multiple testing using Bonferroni’s correction (P < 0.05; Table 4) Linear regression analysis using aforementioned genetic models confirmed that these five polymorphisms affected the variability of pharmacokinetic parameters of ATV, except for the TNF-rs1800629 polymorphism (Additional file 2) Next, we analyzed genotype combinations of the six polymorphisms that individually had a significant effect on ATV pharmacokinetic parameters (Table and Additional file 3): cluster A (subjects with genotypes related to normal metabolism), cluster B (subjects with only genotype related to decreased metabolism), and cluster C (subjects with or more genotypes related to decreased metabolism) As the MTHFR, MDR1, and DRD3 genotypes effected pharmacokinetics most, clusters that only considered these genes were formed: Cluster D (subjects with MTHFR, MDR1 and DRD3 genotypes related to normal metabolism), cluster E (subjects in which either MTHFR, MDR1 or DRD3 gentoype related to decreased metabolism), and cluster F (subjects in which all MTHFR, MDR1 and DRD3 genotypes were related to decreased metabolism) The analysis of genotype combinations revealed that cluster C, i.e subjects with or more genotypes related to decreased metabolism, had a significant higher Cmax (P ≤ 0.016), AUC0-t (P ≤ 0.011) and AUC0-∞ (P = 0.011), but significantly lower Cl/F values (P = 0.011) when compared with cluster A and B The genotype combination analysis limited to MTHFR, MDR1 and DRD3 showed that cluster F was significantly different from clusters D and E; a higher Cmax (P = 5.3 × 10−5), AUC0-t (P = 3.61 × 10−4), and AUC0-∞ = (4.22 × 10−4), but lower Cl/F (P = 1.35 × 10−4) The influences of the clusters on ATV pharmacokinetics are shown in Table The association analysis between clusters and phenotypes displayed a mutual dependency and association (P ≤ 0.05) The C and F cluster were associated with slow metabolizers as shown in Table The stepwise multiple regression analysis showed that the combination of MTHFR, DRD3 and MDR1 polymorphisms are related to ATV slow metabolizers The combination of these three polymorphisms contributed to the pharmacokinetic variability prediction with an R2 = 0.295, and adjusted R2 = 0.257 with a P = 2.26 × 10−4 Adverse effects ATV was well tolerated by all subjects, since no volunteer showed any adverse effects during and at the end of the pharmacokinetic study No clinically significant changes from baseline were observed in the physical examination or the ECG during the study, and no clinically significant mean changes from baseline were observed for any laboratory parameters León-Cachón et al BMC Cancer (2016) 16:74 Page of 13 Table Polymorphisms and genotype clusters with significant effect on ATV pharmacokinetics Pharmacokinetics parameters Genotypes N Cmax (ng/mL) AUC0-t (ng/mL*h) AUC0-∞ (ng/mL*h) Cl/F (L/h) T1/2 (h) Ke MTHFR rs1801133 C/C 14 60.46 ± 20.60 195.77 ± 91.84 213.25 ± 94.16 448.86 ± 202.58 12.95 ± 9.30 0.07 ± 0.04 C/T 37 44.81 ± 24.32* 152.83 ± 87.67 166.43 ± 86.52 595.20 ± 274.47 10.78 ± 5.84 0.08 ± 0.04 T/T 34.89 ± 12.90** 141.64 ± 66.29 151.57 ± 67.28 623.70 ± 287.68 9.94 ± 4.19 0.08 ± 0.03 10.62 ± 5.52 0.08 ± 0.03 C/T + T/T § § § § 46 42.87 ± 22.77 150.64 ± 83.37 163.52 ± 82.64 600.77 ± 274.06 C/C 17 39.90 ± 14.47 149.50 ± 59.86 159.43 ± 57.99 562.72 ± 200.54 C/T 26 44.12 ± 23.77 143.60 ± 85.50 DRD3 rs6280 # 156.77 ± 86.61 §§, # 654.34 316.38 ĐĐ, # ĐĐ, # 8.63 2.33 10.67 6.65 ĐĐ, # 0.08 0.02 0.09 0.04 0.06 ± 0.03§§, T/T 17 58.41 ± 26.70 199.72 ± 102.65 218.89 ± 100.70 431.78 ± 177.83 14.44 ± 8.15 C/C + C/T 43 42.45 ± 20.51 145.93 ± 75.66 157.83 ± 75.82 618.12 ± 277.41 9.86 ± 5.43 0.09 ± 0.04 44.71 ± 21.36 158.00 ± 89.57 171.60 ± 90.03 582.27 ± 270.90 10.92 ± 6.24 0.08 ± 0.03 13.82 ± 10.17 0.08 ± 0.07 # GSTM3 rs1799735 *A/*A *A/*B 55 ¥ 213.93 ± 28.26 ¥ 71.92 ± 32.15 196.08 ± 33.24 G/G 51 45.54 ± 22.80 153.18 ± 82.16 167.14 ± 83.12 586.66 ± 266.42 10.59 ± 6.14 0.08 ± 0.04 G/A 55.88 ± 26.94 215.85 ± 96.68€ 230.57 ± 93.37€ 391.55 ± 132.91€ 14.66 ± 8.45€ 0.06 ± 0.02€ 13 33.43 ± 13.40 135.68 ± 78.72 150.53 ± 79.10 699.77 ± 376.59 10.87 ± 4.99 0.08 ± 0.04 50.78 ± 24.21 168.22 ± 88.36 181.93 ± 88.95 528.14 ± 216.66 11.24 ± 7.00 0.08 ± 0.03 378.87 ± 46.77 TNF rs1800629 MDR1 rs1045642 C/C C/T + T/T 47 Ô SLCO1B1 rs4149056 C/C + C/T 11 53.92 ± 24.72 222.27 ± 91.48 241.23 ± 92.98 390.51 ± 195.46 12.42 ± 8.59 0.07 ± 0.03 T/T 49 45.41 ± 23.01 147.45 ± 80.40≠ 160.29 ± 79.55≠ 604.57 ± 264.87≠ 10.88 ± 6.12 0.08 ± 0.04 P-values of genotype combinations on ATV pharmacokinetics Clusters Cmax AUC0-t AUC0-∞ £ Cl/F T1/2 Ke £ A vs B 0.163 0.060 0.041 0.041 0.519 0.519 B vs C 0.007£ × 10−4£ 0.001£ 0.001£ 0.177 0.177 A vs C £ 0.016 0.011 £ 0.011 0.011 £ 0.181 0.181 C vs A + B 0.001£ 9.1 × 10−5£ 1.31 × 10−4£ 9.5 × 10−5£ 0.112 0.399 £ 0.610 0.486 0.241 0.260 0.230 0.177 0.115 0.176 £ £ D vs E 0.187 0.060 0.041 0.002 E vs F 2.3 × 10−4£ 0.001£ 0.001£ 0.004£ £ £ D vs F F vs D + E −3£ × 10 5.3 × 10−5£ 0.014 0.011 3.61 × 10−4£ 4.22 × 10−4£ −6£ × 10 1.35 × 10−4£ Data presented as mean ± standard deviation *P = 0.018 (C/T vs C/C), **P = 0.004 (T/T vs C/C), §P ≤ 0.050 (C/T + T/T vs C/C), §§P ≤ 0.041 (T/T vs C/T), ¢P ≤ 0.008 (C/C vs T/T), #P ≤ 0.050 (T/T vs C/C + C/T), ¥P = 0.041 (*A/*B vs *A/*A), €P ≤ 0.035 (G/A vs G/G), ÔP = 0.037 (C/T + T/T vs C/C), ≠P = 0.004 (T/T vs C/C + C/T), £ = significant Clusters are explained in the main text Discussion Numerous studies have been performed to better characterize the high variability in ATV pharmacokinetic parameters In this study, the maximum and minimum Cmax and AUC0-t values differed by approximately 10folds in 60 healthy volunteers Other studies even observed 15-folds for the Cmax and 12-folds for AUC, after a single dosis of ATV [32] The interindividual variability exists even though the study subjects are under controlled conditions Actually, the search of prediction biomarkers for disease risk and the response to treatment is an area of research with great activity However, so far no pharmacogenetic testing in clinical studies have been carried out in Mexicans To contribute to the identification of the genetic architecture underlying the drug metabolism and response in the Mexican population, we León-Cachón et al BMC Cancer (2016) 16:74 Table Association values between genotypes and metabolizer phenotypes Association values between genotypes and the slow metabolizer phenotype using dominant, recessive and additive models Gene MTHFR Polymorphism Model OR (95 % CI) P-Value Pc Value rs1801133 Dominant (C/C vs C/T + T/T) C/T + T/T = 0.64 (0.42–0.99)* 0.011 0.028** 0.015 0.034** 0.126 0.308 0.324 0.553 0.008 0.020** 0.610 0.884 C/C = 3.11 (1.26–7.68)* DRD3 rs6280 Recessive (C/C + C/T vs T/T) C/C + C/T = 0.62 (0.38–1.00) T /T = 2.63 (1.21–5.70)* GSTM3 rs1799735 Additive (*A/*A vs *A/*B) *A/*A = 0.88 (0.70–1.09) *A/*B = 3.50 (0.64–19.20) TNF rs1800629 Additive (G/G vs G/A) G/G = 0.89 (0.68–1.16) G/A = 1.82 (0.55–6.00) MDR1 rs1045642 Dominant (C/C vs C/T + T/T) C/T + T/T = 1.45 (1.18–1.77)* C/C = 0.06 (0.003–1.00) SLCO1B1 rs4149056 Recessive (C/C + C/T vs T/T) C/C + C/T = 1.33 (0.45–3.99) T/T = 0.93 (0.70–1.23) OR odds ratio, CI confidence interval, Pc P-values adjusted by using Bonferroni’s correction for multiple comparisons, − = Not calculated, ** = P ≤ 0.05 Clusters are explained in the main text Page of 13 León-Cachón et al BMC Cancer (2016) 16:74 Page of 13 Table Association values between genotypes and metabolizer phenotypes Association between genotype combinations and metabolizer phenotypes Clusters Phenotypes OR (95 % CI) P-Value Pc Value A, B, C Rapid, Normal, Slow - × 10−3** 3.1 × 10−3** C, A + B Slow, Normal + Rapid 4.00 (1.39–11.49)* 3.83 × 10−4** × 10−3** −5 D, E, F Rapid, Normal, Slow - 6.6 × 10 ** 1.6 × 10−3** F, D + E Slow, Normal + Rapid 4.53 (1.62–12.68)* × 10−6** 2.9 × 10−5** OR odds ratio, CI confidence interval, Pc P-values adjusted by using Bonferroni’s correction for multiple comparisons, − = Not calculated, ** = P ≤ 0.05 Clusters are explained in the main text examined the impact of 30 polymorphisms in genes related to drug metabolism and response on ATV pharmacokinetics In this study, we propose a novel and simple approach to classify the metabolizer phenotypes from the analysis of pharmacokinetic profiles This approach uses Cmax, which reflects the absorption rate, and the AUC0-t) that reflects the extent of absorption and clearance We distinguished three different metabolic phenotypes (slow, normal, and rapid) with significant differences for pharmacokinetic parameters (Table 2) The slow phenotype displayed the highest variability in pharmacokinetic parametes, clearly illustrated by the greater dispersion of Cmax-AUC0-t valuesof the slow metabolizers as compared to the normal and rapid ones T1/2 behaves stochastically, and its variance increased with the time, which may be the reason that there is no significant T1/2 difference among metabolizer phenotypes Our phenotype classification is consistent with the one reported by Quing Huang et al., who used a pharmacometabonomic approach to classify 48 healthy volunteers as low, medium, and high ATV metabolizers [32] Slow metabolizers tend to have higher plasma drug levels and to be more susceptible to adverse side effects On the other hand, rapid metabolizers tend to have lower plasma drug levels, which may explain a poor drug response Our results support the use of this classification method for in vivo studies There was a significant effect of six gene polymorphisms on different pharmacokinetics parameters (P ≤ 0.05) T allele carriers of MTHFR-rs1801133 had a lower Cmax and AUC but increased Cl/F This pharmacokinetic profile is consistent with increased clearance activity and a lower ATV concentration in the body, which may lead to a weaker response to ATV To our knowledge, our study is the first that reports an effect of the MTHFR-rs1801133 polymorphism on statin pharmacokinetics, and is consistent with previous studies on drug response and cardiovascular disease susceptibility In 2008, Maitland-van der Zee et al found that the MTHFR-rs1801133 C/C genotype protects against coronary heart disease in different populations [33] Another study reported that the C/C genotype protected against cardiovascular disease in a Turkish population under statin therapy [34] The C allele frequencies reported in both studies [33, 34] are different from the allele frequencies found in the Mexican population The MTHFR-rs1801133 polymorphism (C677T) causes an Ala → Val substitution which decreases enzyme activity leading to increased homocysteine levels Hyperhomocysteinemia is a known risk factor for cardiovascular disease [34] How the T variant of MTHFRrs1801133 augments ATV clearance, so that the drug response is diminished, remains to be elucidated With respect to the DRD3-rs6280 (Ser9Gly) polymorphism, the homozygous variant genotype (T/T) affected the pharmacokinetic parameters consistent with a slow metabolizer phenotype So far, the influence of DRD3-rs6280 on ATV pharmacokinetics has not been reported DRD3 encodes a dopamine receptor and is functionally related to reward stimuli and control of movement [35] DRD3-rs6280 polymorphisms have been mainly related to addictive behavior[36, 37] and involuntary movements [38] The C allele is related to a stronger intracellular response to dopamine [35] However, another DRD3 polymorphism (rs1486012) has been associated with a decrease in lopinavir/ritonavir elimination [39] Subjects that carry the *B allele of GSTM3-rs1799735 had a decreased clearance of ATV and therefore an increased AUC0-∞ These results are not consistent with the higher detoxification activity associated with the *B allele [40] of this gene which encodes a glutathione Stransferase M3 that conjugates glutathione with substrates like drugs, toxins, and carcinogens The *B allele represents a 3-bp deletion in intron 6, which generates a recognition sequence for the Ying Yang transcription factor (YY1), and thus alters the gene expression of GSTM3 We found no previous report relating this polymorphism to statin metabolism The reason for the lack of association of the GSTM3 polymorphism with the metabolic phenotypes may be due to the low frequency of the *B allele in our relatively small study group GSTM3-rs1799735 has been associated with various types of cancers with different effects [40–42] Allele frequencies found for rs1799735 are similar to those León-Cachón et al BMC Cancer (2016) 16:74 reported by Jain et al [42] Nevertheless, the homozygous variant (*B/*B) carrier was absent in our sample Heterozygous A allele carriers of TNF-rs1800629 had pharmacokinetic parameters that were consistent with a diminished clearance of ATV Nevertheless, their influence on ATV pharmacokinetic variability was not confirmed by linear regression analysis The rs1800629 (G/ A) polymorphism is located in the promoter region of TNF TNF encodes the pro-inflammatory cytokine tumor necrosis factor alpha The variant A allele increases protein expression; therefore the wild-type G allele has a protective effect by reducing the risk of thrombosis in patients with hemodialysis [43] It is unknown how TNF is involved in ATV metabolism, but there is evidence that ATV may attenuate TNF expression [44], which results in a reduction of the inflammatory process The allele and genotype frequencies in our study group are similar to those of a Turkish population [43] Previous studies have reported that the MDR1rs1045642 polymorphism affects the response to ATV treatment However, different studies disagree with respect to which homozygous genotype results in a better response to therapy [45–47] MDR1 encodes a Pglycoprotein transporter, that functions as an ATPdependent efflux pump and thus protects against harmful substances MDR1 is widely expressed, for example in the small intestine, the blood–brain barrier, hepatocytes, and kidney proximal tubules [48, 49] The MDR1rs1045642 (C3435T) polymorphism is located in exon 26 of the MDR1/ABCB1 at a wobble position that does not produce an amino acid change However, Hoffmeyer et al [48] have reported that subjects homozygous for the variant (T/T) not only had reduced expression of MDR1 but also higher drug plasma levels as compared to subjects homozygous for the wild-type (C/C) [48] In our study, carriers of the variant T allele, either homozygous or heterozygous, had a higher Cmax Similar results were found by Zhou et al [50] and Gonzalez-Vacarezza et al [51] on fluvastatin and quetiapine pharmacokinetics, respectively The T allele and genotype frequencies found in our study group were similar to those reported in the Lahu population (0.54) [52], the Dutch population (0.52) [53], and in Caucasians (0.53) [54]; however, they differ from those found in the Chilean population (0.34) [16], the African population (0.17), and in AfricanAmericans (0.39) [55] Similar to MDR1 polymorphisms, there are conflicting reports about the response and adverse drug reactions to statins in subjects with the SLCO1B1-rs4149056 (SLCO1B1*5) polymorphism [47, 56] SLCO1B1, which is highly expressed in the liver, encodes an organic anion influx pump for numerous compounds The SLCO1B1rs4149056 (c.T521C) polymorphism results in the Page 10 of 13 substitution of alanine for valine at amino acid residue 174, which reduces transport activity and leads to higher circulating statin concentrations that could be responsible for the reported adverse effects [57] Our results support that rs4149056 affects ATV pharmacokinetics However, the polymorphism did not associate with metabolic phenotypes This could be due to the low frequency of C allele carriers in our sample The variant T allele is the most frequent in Mexican population In summary, in our study we found six polymorphisms in different genes that have a significant effect on the pharmacokinetics of ATV GSTM3 is a phase II metabolizing enzyme, which explains its impact on clearing parameters (Cl/F), and as a consequence AUC MDR1 and SLCO1B1 are transporters, and this function may explain their impacton ATV pharmacokinetic parameters (Cmax, AUC, and Cl/F) However, the significant impact of the DRD3, MTHFR, and TNF polymorphisms on ATV Cmax, AUC’s, Cl/F T1/2, and Ke is achieved by so far unknown mechanisms The value of a pharmacokinetic parameter is the end result of a complex ADME process that involves many proteins Polymorphisms in the different encoding genes may neutralize each other, which makes it highly unlikely that a single polymorphism determines a metabolizer phenotype However, a slow metabolizer phenotype may be the result of various polymorphisms that reinforce a certain impact Indeed the accumulation of polymorphisms of the six genes (MTHFR, GSTM3, DRD3, TNF, MDR1, and SLCO1B1) coincided with a shift from rapid metabolizers (no genotypes related to decreased metabolism in cluster A) to slow metabolizers (cluster C, up to six polymorphisms) MTHFR, MDR1 and DRD3 polymorphisms seem to have a leading impact on metabolizer phenotype, and were sufficient to identify slow metabolizers in our study group For example, cluster D, comprised of MTHFR, MDR1 and DRD3 wild-type genotypes, were rapid metabolizers; cluster E is a mixture of the rapid and normal metabolizers, whereas the accumulation of these three polymorphisms (cluster F) is sufficient to identify slow metabolizers These results were confirmed by association tests with different models, and stepwise multiple regression analysis The interference of other genes in the A and B clusters may explain the lack of association with the rapid metabolizer phenotype This can be explained by the complexity of the process that underlies the pharmacokinetic parameters, which involves many genes and other environmental factors Thus, if a gene variant has a relatively small impact it will not be detected The distribution plot (Fig 2b) illustrates that slow metabolizer phenotype accumulates the widest variability of polymorphisms Although we found six polymorphism with effect on the ATV pharmacokinetics, we León-Cachón et al BMC Cancer (2016) 16:74 know that the variability is not limited to the presence of these six markers, since other polymorphisms with little or moderate influence and not analyzed in this study, could contribute to the observed variability in each metabolic phenotype To our knowledge, this is the first report on the use of a massive genotyping tool (microarrays) to associate gene polymorphisms with pharmacokinetic variability of a drug commonly used by the Mexican population The pharmacokinetic variability of ATV depends on several factors, including genetic factors We identified six polymorphisms on six different genes that, individually, had an impact on some or all pharmacokinetic parameters Absence of all polymorphism corresponded to rapid metabolizers, whereas the accumulation of polymorphisms caused a shift to slow metabolizers However, some shortcomings existed in our study First, there were insufficient data to correlate the metabolic phenotypes with the ATV response Second, the number of participants was not large enough to validate our findings Hence, our findings need to be validated in a larger population with genotype preselection In order to identify (a set of ) candidate predictors for ATV metabolizer phenotype, confirmative studies should be performed that focuse on the identified six polymorphisms Our results may also be considered for future meta-analysis The ultimate aim is that a pharmacogenetic analysis of a set of genes can be used to guide a personalized dosage that ensures drug response and prevents adverse drug effects Conclusions In summary, this pilot study offers a novel, comprehensive approach to understand the genetic contribution to the variability of ATV metabolism in a Mexican population It enabled the identification of candidate predictive biomarkers for slow ATV metabolizers The future confirmation of the predictive potential of these candidate genetic biomarkers and their incorporation in routine genotyping tests may optimize ATV efficacy in the clinical practice Additional files Additional file 1: Allele and genotype frequencies of polymorphism in genes involved in drug metabolism and response HWE = HardyWeinberg equilibrium, P = gene present, A = gene absent, ǂ = genotypes with frequency < 0.01, ¥ = not calculated (DOCX 33 kb) Additional file 2: The P-values of results of linear regression for polymorphisms on ATV pharmacokinetic parameters using different genetic models a = Dominant (C/C vs C/T + T/T), b = Recessive (C/C + C/T vs T/T), c = Additive (*A/*A vs *A/*B), d = Additive (G/G vs G/A), e = Additive (G/G vs G/A), f = Recessive (C/C + C/T vs T/T), Pc = P-values adjusted by using Bonferroni’s correction for multiple comparisons, − = Not significant, * = Significant (DOC 36 kb) Page 11 of 13 Additional file 3: Genotype clusters Genotype combinations are classified as follows: altered genotypes are in red and other genotypes are in green; Cluster A:subjects with genotypes related to normal metabolism, cluster B: subjects with only genotype related to decreased metabolism, cluster C: subjects with or more genotypes related to decreased metabolism, cluster D: subjects with MDR1 and DRD3 genotypes related to normal metabolism, cluster E: subjects in which either MDR1 or DRD3 was altered, and cluster F: subjects in which both MDR1 and DRD3 were altered (TIFF 3555 kb) Abbreviations ATV: Atorvastatin; ADME: Absorption, distribution, metabolism, and excretion processes; BMI: Body mass index; ECG: Electrocardiogram; HWE: HardyWeinberg equilibrium; MAF: Minor allele frequency; Cmax: Maximum plasma concentration; AUC0-t: Area under the plasma concentration-time curve from time to the time of the last measurement; AUC0-∞: Area under the curve from time to the time extrapolated to infinity; Cl/F: Apparent clearance of the fraction dose absorbed; Ke: Elimination rate constant in the terminal phase; T1/2: Half-life in the terminal phase of the drug; HCA: Hierarchical cluster analysis Competing interests We wish to confirm that there are no known conflicts of interest associated with this publication and the financial support for this work has not influenced its outcome Authors’ contributions Designed research and conception: RBRLC, HABRS, JAAM, MGS, and EPG; Performed research: RBRLC, JAAM, MGS, and EPG; Pharmacokinetic analysis data: MEGP; Analyzed data: RBRLC, MEGP, RMCF, and HLGB; Data interpretation: RBRLC, RMCF, IM, and HLGB; Wrote manuscript: RBRLC, IM, and HABRS; Edited manuscript: RBRLC, IM, and HABS All authors read and approved the final manuscript Acknowledgements Research was mainly supported by Ipharma S.A., Monterrey, Mexico We thank the technical staff of Ipharma, S.A and Vitagenesis, S.A for facilities, advice and technical support We also thank Lourdes Garza-Rodríguez and Herminia G Martínez-Rodríguez for valuable suggestions Author details Centro de Diagnóstico Molecular y Medicina Personalizada, Departamento de Ciencias Básicas, División Ciencias de la Salud, Universidad de Monterrey, San Pedro Garza García, NL, México 2Departamento de Bioquímica y Medicina Molecular, Facultad de Medicina, Universidad Autónoma de Nuevo León, Monterrey, NL, México 3Ipharma S.A., Monterrey, NL, México 4Facultad de Enfermería, Universidad Autónoma de Nuevo León, Monterrey, NL, México 5Departamento de Genética, Universidad Autónoma de Nuevo León, Monterrey, NL, México 6Vitagénesis S.A., Monterrey, NL, México Received: 26 February 2015 Accepted: 10 January 2016 References Lennernas H Clinical pharmacokinetics of atorvastatin Clin Pharmacokinet 2003;42(13):1141–60 doi:10.2165/00003088-200342130-00005 Kivisto KT, Niemi M, Schaeffeler E, Pitkala K, Tilvis R, Fromm MF, et al Lipidlowering response to statins is affected by CYP3A5 polymorphism Pharmacogenetics 2004;14(8):523–5 doi:00008571-200408000-00005 Ahmed T, Kollipara S, Gautam A, Gigras R, Kothari M, Saha N, et al Bioavailability and Interaction Potential of Atorvastatin and Losartan on Coadministration in Healthy Human Subjects J Bioequivalence Bioavailability 2009;1(1):018–27 doi:10.4172/jbb.1000004 Jacobsen W, Kuhn B, Soldner A, Kirchner G, Sewing KF, Kollman PA, et al Lactonization is the critical first step in the disposition of the 3-hydroxy-3methylglutaryl-CoA reductase inhibitor atorvastatin Drug Metab Dispos 2000;28(11):1369–78 Riedmaier S, Klein K, Hofmann U, Keskitalo JE, Neuvonen PJ, Schwab M, et al UDP-glucuronosyltransferase (UGT) polymorphisms affect atorvastatin León-Cachón et al BMC Cancer (2016) 16:74 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 lactonization in vitro and in vivo Clin Pharmacol Ther 2010;87(1):65–73 doi:10.1038/clpt.2009.181 Chen C, Mireles RJ, Campbell SD, Lin J, Mills JB, Xu JJ, et al Differential interaction of 3-hydroxy-3-methylglutaryl-coa reductase inhibitors with ABCB1, ABCC2, and OATP1B1 Drug Metab Dispos 2005;33(4):537–46 doi:10.1124/dmd.104.002477 Romaine SP, Bailey KM, Hall AS, Balmforth AJ The influence of SLCO1B1 (OATP1B1) gene polymorphisms on response to statin therapy Pharmacogenomics J 2010;10(1):1–11 doi:10.1038/tpj.2009.54tpj200954 Schwarz UI Meyer zu Schwabedissen HE, Tirona RG, Suzuki A, Leake BF, Mokrab Y et al Identification of novel functional organic anion-transporting polypeptide 1B3 polymorphisms and assessment of substrate specificity Pharmacogenet Genomics 2011;21(3):103–14 doi:10.1097/FPC.0b013e328342f5b1 Keskitalo JE, Zolk O, Fromm MF, Kurkinen KJ, Neuvonen PJ, Niemi M ABCG2 polymorphism markedly affects the pharmacokinetics of atorvastatin and rosuvastatin Clin Pharmacol Ther 2009;86(2):197–203 doi:10.1038/clpt.2009.79 Mangravite LM, Thorn CF, Krauss RM Clinical implications of pharmacogenomics of statin treatment Pharmacogenomics J 2006;6(6): 360–74 doi:10.1038/sj.tpj.6500384.6500384 U.S Food and Drug Administration Guidance for Industry Clinical Pharmacogenomics: Premarket Evaluation in Early-Phase Clinical Studies and Recommendations for Labeling.Silver Spring: U.S Department of Health and Human Services; 2013 Leon-Cachon RB, Ascacio-Martinez JA, Barrera-Saldana HA Individual response to drug therapy: bases and study approaches Rev Invest Clin 2012;64(4):364–76 U.S Food and Drug Administration Draft Guidance on Atorvastatin Calcium and Ezetimibe Silver Spring: U.S Department of Health and Human Services; 2014 Gandelman K, Malhotra B, LaBadle R, Crownover P, Bergstrom T Analytes of Interest and Choice of Dose: Two Important Considerations in the Design of Bioequivalence Studies with Atorvastatin J Bioequiv Availab 2011;3(4):062– doi:10.4172/jbb.1000060 Cavallari L, Lam Y Pharmacogenetics In: DiPiro J, Talbert R, Yee G, Matzke G, Wells B, Posey L, eds Pharmacotherapy: A Pathophysiologic Approach 9a ed New York, NY: McGraw-Hill; 2014 p 2559 Rosales A, Alvear M, Cuevas A, Saavedra N, Zambrano T, Salazar LA Identification of pharmacogenetic predictors of lipid-lowering response to atorvastatin in Chilean subjects with hypercholesterolemia Clin Chim Acta Intl J Clin Chem 2012;413(3–4):495–501 doi:10.1016/j.cca.2011.11.003 Eichelbaum M, Ingelman-Sundberg M, Evans WE Pharmacogenomics and individualized drug therapy Annu Rev Med 2006;57:119–37 doi:10.1146/ annurev.med.56.082103.104724 Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, et al A map of human genome variation from population-scale sequencing Nature 2010;467(7319):1061–73 doi:10.1038/nature09534 nature09534 Madsen BE, Villesen P, Wiuf C Short tandem repeats and genetic variation Methods Mol Biol 2010;628:297–306 doi:10.1007/978-1-60327-367-1_16 Pang AW, MacDonald JR, Pinto D, Wei J, Rafiq MA, Conrad DF, et al Towards a comprehensive structural variation map of an individual human genome Genome Biol 2010;11(5):R52 doi:10.1186/gb-2010-11-5-r52.gb2010-11-5-r52 Hirschhorn JN, Daly MJ Genome-wide association studies for common diseases and complex traits Nat Rev Genet 2005;6(2):95–108 doi:10.1038/ nrg1521 nrg1521 Solorzano-Flores LI NORMA Oficial Mexicana NOM-177-SSA1-1998, Que establece las pruebas y procedimientos para demostrar que un medicamento es intercambiable Requisitos a que deben sujetarse los terceros autorizados que realicen las pruebas Secretaría de Salud, México, DF 1999 http://www.salud.gob.mx/unidades/cdi/nom/177ssa18.html 2010 Ma L, Dong J, Chen X, Wang G Development and validation of atorvastatin by LC–ESI–MS and application in bioequivalence research in healthy Chinese volunteers Chromatographia 2007;65(11):737–41 doi:10.1365/s10337-007-0236-4 Stanisz B, Kania L Validation of HPLC method for determination of atorvastatin in tablets and for monitoring stability in solid phase Acta Pol Pharm 2006;63(6):471–6 Sambrook J Russell D W Preparation and Analysis of Eukaryotic Genomic DNA Molecular cloning: a laboratory manual Third ed Cold Spring Harbor: Cold Spring Harbor Laboratory Press; 2001 Tejedor D, Castillo S, Mozas P, Jimenez E, Lopez M, Tejedor MT, et al Reliable low-density DNA array based on allele-specific probes for detection of 118 mutations causing familial hypercholesterolemia Clin Chem 2005; 51(7):1137–44 doi:10.1373/clinchem.2004.045203 Page 12 of 13 27 Miyaji K, Nagao K, Bannai M, Asakawa H, Kohyama K, Ohtsu D, et al Characteristic metabolism of free amino acids in cetacean plasma: cluster analysis and comparison with mice PLoS One 2010;5(11):e13808 doi:10 1371/journal.pone.0013808 28 Reed TE, Schull WJ A general maximum likelihood estimation program Am J Hum Genet 1968;20(6):579–80 29 Benjamini Y, Hochberg Y Controlling the false discovery rate: a practical and powerful approach to multiple testing J R Stat Soc Ser B Methodol 1995;57(1):289–300 30 Endrenyi L, Fritsch S, Yan W Cmax/AUC is a clearer measure than Cmax for absorption rates in investigations of bioequivalence Int J Clin Pharmacol Ther Toxicol 1991;29(10):394–9 31 Burim RV, Canalle R, Martinelli Ade L, Takahashi CS Polymorphisms in glutathione S-transferases GSTM1, GSTT1 and GSTP1 and cytochromes P450 CYP2E1 and CYP1A1 and susceptibility to cirrhosis or pancreatitis in alcoholics Mutagenesis 2004;19(4):291–8 32 Huang Q, Aa J, Jia H, Xin X, Tao C, Liu L et al A Pharmacometabonomic Approach to Predicting Metabolic Phenotypes and Pharmacokinetic Parameters of Atorvastatin in Healthy Volunteers Journal of proteome research 2015 doi:10.1021/acs.jproteome.5b00440 33 Maitland-van der Zee A-H, Lynch A, Boerwinkle E, Arnett DK, Davis BR, Leiendecker-Foster C, et al Interactions between the SNPs in the homocysteine pathway (MTHFR 677C > T, MTHFR 1298 A > C and CBSins) and the efficacy of HMG-CoA reductase inhibitors in preventing cardiovascular disease in high-risk hypertensives: The GenHAT Study Pharmacogenet Genomics 2008;18(8):651–6 doi:10.1097/FPC 0b013e3282fe1759 34 Izmirli M, Bacaksiz A, Alptekin D, Uysal O, Kilic U Statin and MTHFR C677T Polymorphism in Patients with Cardiovascular Diseases Bezmialem Sci 2014;2014(1):38–41 35 Savitz J, Hodgkinson CA, Martin-Soelch C, Shen PH, Szczepanik J, Nugent A, et al The functional DRD3 Ser9Gly polymorphism (rs6280) is pleiotropic, affecting reward as well as movement PLoS One 2013;8(1):e54108 doi:10 1371/journal.pone.0054108 36 Kang SG, Lee BH, Lee JS, Chai YG, Ko KP, Lee HJ, et al DRD3 gene rs6280 polymorphism may be associated with alcohol dependence overall and with Lesch type I alcohol dependence in Koreans Neuropsychobiology 2014;69(3):140–6 doi:10.1159/000358062 37 Kuo SC, Yeh YW, Chen CY, Huang CC, Chang HA, Yen CH, et al DRD3 variation associates with early-onset heroin dependence, but not specific personality traits Prog Neuropsychopharmacol Biol Psychiatry 2014;51:1–8 doi:10.1016/j.pnpbp.2013.12.018 38 Jimenez-Jimenez FJ, Alonso-Navarro H, Martinez C, Zurdo M, Turpin-Fenoll L, Millan J, et al Dopamine receptor D3 (DRD3) gene rs6280 variant and risk for restless legs syndrome Sleep Med 2013;14(4):382–4 doi:10.1016/j.sleep 2012.11.009 39 Lopez Aspiroz E, Cabrera Figueroa SE, Porras Hurtado GL, Cruz Guerrero R, Dominguez-Gil Hurle A, Carracedo A, et al Pharmacogenetic analysis of SNPs in genes involved in the pharmacokinetics and response to lopinavir/ ritonavir therapy Curr Drug Metab 2013;14(7):729–37 40 Xu Y, Wang J, Dong W GSTM3 A/B polymorphism and risk for head and neck cancer: a meta-analysis PLoS One 2014;9(1):e83851 doi:10.1371/ journal.pone.0083851 41 Salinas-Souza C, Petrilli AS, de Toledo SR Glutathione S-transferase polymorphisms in osteosarcoma patients Pharmacogenet Genomics 2010; 20(8):507–15 doi:10.1097/FPC.0b013e32833caa45 42 Jain M, Kumar S, Lal P, Tiwari A, Ghoshal UC, Mittal B Role of GSTM3 polymorphism in the risk of developing esophageal cancer Cancer Epidemiol Biomarkers Prev 2007;16(1):178–81 doi:10.1158/1055-9965.EPI-06-0542 43 Sener EF, Taheri S, Korkmaz K, Zararsiz G, Serhatlioglu F, Unal A, et al Association of TNF-alpha −308 G > A and ACE I/D gene polymorphisms in hemodialysis patients with arteriovenous fistula thrombosis Int Urol Nephrol 2014;46(7):1419–25 doi:10.1007/s11255-013-0580-2 44 Sun YM, Tian Y, Li X, Liu YY, Wang LF, Li J, et al Effect of atorvastatin on expression of IL-10 and TNF-alpha mRNA in myocardial ischemiareperfusion injury in rats Biochem Biophys Res Commun 2009;382(2):336– 40 doi:10.1016/j.bbrc.2009.03.019 45 Kajinami K, Brousseau ME, Ordovas JM, Schaefer EJ Polymorphisms in the multidrug resistance-1 (MDR1) gene influence the response to atorvastatin treatment in a gender-specific manner Am J Cardiol 2004;93(8):1046–50 doi:10.1016/j.amjcard.2004.01.014 León-Cachón et al BMC Cancer (2016) 16:74 Page 13 of 13 46 Hoenig MR, Walker PJ, Gurnsey C, Beadle K, Johnson L The C3435T polymorphism in ABCB1 influences atorvastatin efficacy and muscle symptoms in a high-risk vascular cohort J Clin Lipidol 2011;5(2):91–6 doi:10.1016/j.jacl.2011.01.001 47 Shabana MF, Mishriki AA, Issac MS, Bakhoum SW Do MDR1 and SLCO1B1 polymorphisms influence the therapeutic response to atorvastatin? A study on a cohort of Egyptian patients with hypercholesterolemia Mol Diagn Ther 2013;17(5):299–309 doi:10.1007/s40291-013-0038-3 48 Hoffmeyer S, Burk O, von Richter O, Arnold HP, Brockmoller J, Johne A, et al Functional polymorphisms of the human multidrug-resistance gene: multiple sequence variations and correlation of one allele with Pglycoprotein expression and activity in vivo Proc Natl Acad Sci U S A 2000; 97(7):3473–8 doi:10.1073/pnas.050585397 49 Wessler JD, Grip LT, Mendell J, Giugliano RP The P-glycoprotein transport system and cardiovascular drugs J Am Coll Cardiol 2013;61(25):2495–502 doi:10.1016/j.jacc.2013.02.058 50 Zhou Q, Ruan ZR, Yuan H, Zeng S CYP2C9*3(1075A > C), MDR1 G2677T/A and MDR1 C3435T are determinants of inter-subject variability in fluvastatin pharmacokinetics in healthy Chinese volunteers Arzneimittelforschung 2012;62(11):519–24 doi:10.1055/s-0032-1323696 51 Gonzalez-Vacarezza N, Dorado P, Penas-Lledo EM, Farinas H, Estevez-Carrizo FE, Llerena A MDR-1 genotypes and quetiapine pharmacokinetics in healthy volunteers Drug Metabol Drug Interact 2013;28(3):163–6 doi:10 1515/dmdi-2013-0008 52 Dong Q, Xu B, Tan Y, Liu Z, Tian L, Zhang B, et al The genetic variability of MDR1 C3435T polymorphisms in four Southern Chinese populations Biomed Pharmacother 2009;63(9):658–62 doi:10.1016/j.biopha.2008.09.003 53 Becker ML, Visser LE, van Schaik RH, Hofman A, Uitterlinden AG, Stricker BH Influence of genetic variation in CYP3A4 and ABCB1 on dose decrease or switching during simvastatin and atorvastatin therapy Pharmacoepidemiol Drug Saf 2010;19(1):75–81 doi:10.1002/pds.1866 54 Henningsson A, Marsh S, Loos WJ, Karlsson MO, Garsa A, Mross K, et al Association of CYP2C8, CYP3A4, CYP3A5, and ABCB1 polymorphisms with the pharmacokinetics of paclitaxel Clin Cancer Res 2005;11(22):8097–104 doi:10.1158/1078-0432.CCR-05-1152 55 Schaeffeler E, Eichelbaum M, Brinkmann U, Penger A, Asante-Poku S, Zanger UM, et al Frequency of C3435T polymorphism of MDR1 gene in African people Lancet 2001;358(9279):383–4 doi:10.1016/S0140-6736(01)05579-9 56 Santos PC, Gagliardi AC, Miname MH, Chacra AP, Santos RD, Krieger JE, et al SLCO1B1 haplotypes are not associated with atorvastatin-induced myalgia in Brazilian patients with familial hypercholesterolemia Eur J Clin Pharmacol 2012;68(3):273–9 doi:10.1007/s00228-011-1125-1 57 Pasanen MK, Fredrikson H, Neuvonen PJ, Niemi M Different effects of SLCO1B1 polymorphism on the pharmacokinetics of atorvastatin and rosuvastatin Clin Pharmacol Ther 2007;82(6):726–33 doi:10.1038/sj.clpt 6100220 Submit your next manuscript to BioMed Central and we will help you at every step: • We accept pre-submission inquiries • Our selector tool helps you to find the most relevant journal • We provide round the clock customer support • Convenient online submission • Thorough peer review • Inclusion in PubMed and all major indexing services • Maximum visibility for your research Submit your manuscript at www.biomedcentral.com/submit ... Kollipara S, Gautam A, Gigras R, Kothari M, Saha N, et al Bioavailability and Interaction Potential of Atorvastatin and Losartan on Coadministration in Healthy Human Subjects J Bioequivalence Bioavailability... has been associated with a decrease in lopinavir/ritonavir elimination [39] Subjects that carry the *B allele of GSTM3-rs1799735 had a decreased clearance of ATV and therefore an increased AUC0-∞... on clearing parameters (Cl/F), and as a consequence AUC MDR1 and SLCO1B1 are transporters, and this function may explain their impacton ATV pharmacokinetic parameters (Cmax, AUC, and Cl/F) However,